{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/shrutilakhtakia/Desktop/Replication Files/replication.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}26 Aug 2023, 08:46:37
{txt}
{com}. use data_replication_2018.dta, replace
{txt}
{com}. * PART 1. MAIN RESULTS *
. 
. * Table 1. Lower Turnout for Females in Separate-Gender Polling Stations *
. 
. * Full Sample Regressions: Columns 1 and 2 *
. eststo clear
{txt}
{com}. 
. reg female_turnout_ps female if assembly_cat==1, cluster(id)

{txt}Linear regression                               Number of obs     = {res}    51,724
                                                {txt}F(1, 46184)       =  {res}  1652.61
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0327
                                                {txt}Root MSE          =    {res}  .1515

{txt}{ralign 78:(Std. Err. adjusted for {res:46,185} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}female_tur~s{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}female {c |}{col 14}{res}{space 2}-.0588551{col 26}{space 2} .0014478{col 37}{space 1}  -40.65{col 46}{space 3}0.000{col 54}{space 4}-.0616928{col 67}{space 3}-.0560175
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5116585{col 26}{space 2} .0009526{col 37}{space 1}  537.12{col 46}{space 3}0.000{col 54}{space 4} .5097914{col 67}{space 3} .5135256
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. su female_turnout_ps if e(sample) & assembly_cat==1 & female==0

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
female_tur~s {c |}{res}     34,188    .5116585    .1588773          0          1
{txt}
{com}. estadd local building "No"

{txt}added macro:
           e(building) : "{res:No}"

{com}. eststo Model1
{txt}
{com}. estimates store main_1_f
{txt}
{com}. 
. areg female_turnout_ps female if assembly_cat==1, cluster(id) absorb(id)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    51,724
{txt}{col 49}F({res}   1{txt},{res}  46184{txt}){col 67}= {res}      3.92
{txt}{col 49}Prob > F{col 67}= {res}    0.0476
{txt}{col 49}R-squared{col 67}= {res}    0.9825
{txt}{col 49}Adj R-squared{col 67}= {res}    0.8364
{txt}{col 49}Root MSE{col 67}= {res}    0.0623

{txt}{ralign 78:(Std. Err. adjusted for {res:46,185} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}female_tur~s{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}female {c |}{col 14}{res}{space 2}-.0206693{col 26}{space 2} .0104357{col 37}{space 1}   -1.98{col 46}{space 3}0.048{col 54}{space 4}-.0411235{col 67}{space 3}-.0002152
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4987123{col 26}{space 2}  .003538{col 37}{space 1}  140.96{col 46}{space 3}0.000{col 54}{space 4} .4917777{col 67}{space 3} .5056469
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          id {c |}   absorbed                                   (46185 categories)

{com}. su female_turnout_ps if e(sample) & assembly_cat==1 & female==0

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
female_tur~s {c |}{res}     34,188    .5116585    .1588773          0          1
{txt}
{com}. estadd local building "Yes"

{txt}added macro:
           e(building) : "{res:Yes}"

{com}. eststo Model2
{txt}
{com}. estimates store main_2_f
{txt}
{com}. 
. * Restricted Sample Regressions (by location type): Columns 3 & 4 *
. 
. areg female_turnout_ps female if assembly_cat==1 & two_types==1, cluster(id) absorb(id)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     6,878
{txt}{col 49}F({res}   1{txt},{res}   5646{txt}){col 67}= {res}      6.55
{txt}{col 49}Prob > F{col 67}= {res}    0.0105
{txt}{col 49}R-squared{col 67}= {res}    0.9597
{txt}{col 49}Adj R-squared{col 67}= {res}    0.7747
{txt}{col 49}Root MSE{col 67}= {res}    0.0568

{txt}{ralign 78:(Std. Err. adjusted for {res:5,647} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}female_tur~s{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}female {c |}{col 14}{res}{space 2}-.0206693{col 26}{space 2} .0080749{col 37}{space 1}   -2.56{col 46}{space 3}0.011{col 54}{space 4}-.0364992{col 67}{space 3}-.0048394
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4986989{col 26}{space 2} .0067447{col 37}{space 1}   73.94{col 46}{space 3}0.000{col 54}{space 4} .4854766{col 67}{space 3} .5119211
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          id {c |}   absorbed                                    (5647 categories)

{com}. su female_turnout_ps if e(sample) & assembly_cat==1 & female==0 & two_types==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
female_tur~s {c |}{res}      1,133    .4664426    .1328982          0          1
{txt}
{com}. estadd local building "Yes"

{txt}added macro:
           e(building) : "{res:Yes}"

{com}. eststo Model3
{txt}
{com}. estimates store main_3_f
{txt}
{com}. 
. areg female_turnout_ps female if assembly_cat==1 & all_types==1, cluster(id) absorb(id)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     1,144
{txt}{col 49}F({res}   1{txt},{res}    502{txt}){col 67}= {res}     13.24
{txt}{col 49}Prob > F{col 67}= {res}    0.0003
{txt}{col 49}R-squared{col 67}= {res}    0.8536
{txt}{col 49}Adj R-squared{col 67}= {res}    0.7386
{txt}{col 49}Root MSE{col 67}= {res}    0.0607

{txt}{ralign 78:(Std. Err. adjusted for {res:503} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}female_tur~s{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}female {c |}{col 14}{res}{space 2}-.0188581{col 26}{space 2}  .005183{col 37}{space 1}   -3.64{col 46}{space 3}0.000{col 54}{space 4}-.0290411{col 67}{space 3}-.0086751
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .461774{col 26}{space 2} .0025371{col 37}{space 1}  182.01{col 46}{space 3}0.000{col 54}{space 4} .4567894{col 67}{space 3} .4667587
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          id {c |}   absorbed                                     (503 categories)

{com}. su female_turnout_ps if e(sample) & assembly_cat==1 & female==0 & all_types==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
female_tur~s {c |}{res}        584    .4571846    .1268991   .0016502          1
{txt}
{com}. estadd local building "Yes"

{txt}added macro:
           e(building) : "{res:Yes}"

{com}. eststo Model4
{txt}
{com}. estimates store main_4_f
{txt}
{com}. 
. esttab Model1 Model2 Model3 Model4 using turnout_female.tex, label replace booktabs keep(female) ///
>  b(3) se(3) sfmt(3)  title(Female Turnout\label{c -(}tab:turnout_female{c )-}) ///
>  scalars("r2_a Adjusted R\textsuperscript{c -(}2{c )-}" "building Location Fixed Effects") mtitle("" "" "" "") ///
>  unstack collabels(none) order(female) star(* 0.10 ** 0.05 *** 0.01)
{res}{txt}(note: file turnout_female.tex not found)
(output written to {browse  `"turnout_female.tex"'})

{com}.  
. * PART 2. ONLINE APPENDIX *
. 
. * Online Appendix Table 1. Descriptive Statistics by PS Type *
. 
. preserve
{txt}
{com}. 
. putexcel set "Table_1_new.xlsx", sheet(Sheet1) replace
{res}{txt}
{com}. putexcel A1 = ("") B1 = ("Overall")  C1 = ("Female-Only") D1 = ("Male-Only") E1 = ("Combined")
{res}{txt}file Table_1_new.xlsx saved

{com}. putexcel A2 = ("Total Voters") A4 = ("Female Registered Voters") A6 = ("Female Votes Cast") A8 = ("Male Registered Voters") A10 = ("Male Votes Cast") A12 = ("Share of Valid Votes (\%)")  
{res}{txt}file Table_1_new.xlsx saved

{com}. 
. * Overall
. tabstat total_voters female_voters female_votes male_voters male_votes valid_share if assembly_cat==1, stat(mean sd) col(stat) save

{txt}{ralign 12:variable} {...}
{c |}      mean        sd
{hline 13}{c +}{hline 20}
{ralign 12:total_voters} {...}
{c |}{...}
 {res} 1268.316  461.8702
{txt}{ralign 12:female_vo~rs} {...}
{c |}{...}
 {res} 549.7334  457.4279
{txt}{ralign 12:female_votes} {...}
{c |}{...}
 {res} 256.8956  220.5346
{txt}{ralign 12:male_voters} {...}
{c |}{...}
 {res} 698.0819  533.6041
{txt}{ralign 12:male_votes} {...}
{c |}{...}
 {res} 386.6413  304.2485
{txt}{ralign 12:valid_share} {...}
{c |}{...}
 {res} 96.67204  3.123199
{txt}{hline 13}{c BT}{hline 20}

{com}. matrix desc_stat = r(StatTotal)
{txt}
{com}. gen mean_1 = desc_stat[1,1]
{txt}
{com}. gen mean_2 = desc_stat[1,2]
{txt}
{com}. gen mean_3 = desc_stat[1,3]
{txt}
{com}. gen mean_4 = desc_stat[1,4]
{txt}
{com}. gen mean_5 = desc_stat[1,5]
{txt}
{com}. gen mean_6 = desc_stat[1,6]
{txt}
{com}. 
. putexcel B2 = (mean_1) B4 = (mean_2) B6 = (mean_3) B8 = (mean_4) B10 = (mean_5) B12 = (mean_6)  
{res}{txt}file Table_1_new.xlsx saved

{com}. 
. gen sd_1 = desc_stat[2,1]
{txt}
{com}. gen sd_2 = desc_stat[2,2]
{txt}
{com}. gen sd_3 = desc_stat[2,3]
{txt}
{com}. gen sd_4 = desc_stat[2,4]
{txt}
{com}. gen sd_5 = desc_stat[2,5]
{txt}
{com}. gen sd_6 = desc_stat[2,6]
{txt}
{com}. 
. putexcel B3 = (sd_1) B5 = (sd_2) B7 = (sd_3) B9 = (sd_4) B11 = (sd_5) B13 = (sd_6)  
{res}{txt}file Table_1_new.xlsx saved

{com}. 
. * Female-Only
. tabstat total_voters female_voters female_votes male_voters male_votes valid_share if assembly_cat==1 & female==1, stat(mean sd) col(stat) save

{txt}{ralign 12:variable} {...}
{c |}      mean        sd
{hline 13}{c +}{hline 20}
{ralign 12:total_voters} {...}
{c |}{...}
 {res}  1154.52  437.0551
{txt}{ralign 12:female_vo~rs} {...}
{c |}{...}
 {res} 1119.314    336.03
{txt}{ralign 12:female_votes} {...}
{c |}{...}
 {res} 497.2158  188.6298
{txt}{ralign 12:male_voters} {...}
{c |}{...}
 {res}        0         0
{txt}{ralign 12:male_votes} {...}
{c |}{...}
 {res}        0         0
{txt}{ralign 12:valid_share} {...}
{c |}{...}
 {res} 95.87081  3.930685
{txt}{hline 13}{c BT}{hline 20}

{com}. matrix desc_stat = r(StatTotal)
{txt}
{com}. gen mean_11 = desc_stat[1,1]
{txt}
{com}. gen mean_12 = desc_stat[1,2]
{txt}
{com}. gen mean_13 = desc_stat[1,3]
{txt}
{com}. gen mean_14 = desc_stat[1,4]
{txt}
{com}. gen mean_15 = desc_stat[1,5]
{txt}
{com}. gen mean_16 = desc_stat[1,6]
{txt}
{com}. 
. putexcel C2 = (mean_11) C4 = (mean_12) C6 = (mean_13) C8 = (mean_14) C10 = (mean_15) C12 = (mean_16)  
{res}{txt}file Table_1_new.xlsx saved

{com}. 
. gen sd_11 = desc_stat[2,1]
{txt}
{com}. gen sd_12 = desc_stat[2,2]
{txt}
{com}. gen sd_13 = desc_stat[2,3]
{txt}
{com}. gen sd_14 = desc_stat[2,4]
{txt}
{com}. gen sd_15 = desc_stat[2,5]
{txt}
{com}. gen sd_16 = desc_stat[2,6]
{txt}
{com}. 
. putexcel C3 = (sd_11) C5 = (sd_12) C7 = (sd_13) C9 = (sd_14) C11 = (sd_15) C13 = (sd_16) 
{res}{txt}file Table_1_new.xlsx saved

{com}. 
. * Male-Only
. tabstat total_voters female_voters female_votes male_voters male_votes valid_share if assembly_cat==1 & male==1, stat(mean sd) col(stat) save

{txt}{ralign 12:variable} {...}
{c |}      mean        sd
{hline 13}{c +}{hline 20}
{ralign 12:total_voters} {...}
{c |}{...}
 {res} 1334.419  472.6098
{txt}{ralign 12:female_vo~rs} {...}
{c |}{...}
 {res}        0         0
{txt}{ralign 12:female_votes} {...}
{c |}{...}
 {res}        0         0
{txt}{ralign 12:male_voters} {...}
{c |}{...}
 {res}  1294.84  370.3715
{txt}{ralign 12:male_votes} {...}
{c |}{...}
 {res} 720.2075  228.3935
{txt}{ralign 12:valid_share} {...}
{c |}{...}
 {res} 97.62116  1.982125
{txt}{hline 13}{c BT}{hline 20}

{com}. matrix desc_stat = r(StatTotal)
{txt}
{com}. gen mean_21 = desc_stat[1,1]
{txt}
{com}. gen mean_22 = desc_stat[1,2]
{txt}
{com}. gen mean_23 = desc_stat[1,3]
{txt}
{com}. gen mean_24 = desc_stat[1,4]
{txt}
{com}. gen mean_25 = desc_stat[1,5]
{txt}
{com}. gen mean_26 = desc_stat[1,6]
{txt}
{com}. 
. putexcel D2 = (mean_21) D4 = (mean_22) D6 = (mean_23) D8 = (mean_24) D10 = (mean_25) D12 = (mean_26)  
{res}{txt}file Table_1_new.xlsx saved

{com}. 
. gen sd_21 = desc_stat[2,1]
{txt}
{com}. gen sd_22 = desc_stat[2,2]
{txt}
{com}. gen sd_23 = desc_stat[2,3]
{txt}
{com}. gen sd_24 = desc_stat[2,4]
{txt}
{com}. gen sd_25 = desc_stat[2,5]
{txt}
{com}. gen sd_26 = desc_stat[2,6]
{txt}
{com}. 
. putexcel D3 = (sd_21) D5 = (sd_22) D7 = (sd_23) D9 = (sd_24) D11 = (sd_25) D13 = (sd_26) 
{res}{txt}file Table_1_new.xlsx saved

{com}. 
. * Combined
. tabstat total_voters female_voters female_votes male_voters male_votes valid_share if assembly_cat==1 & female==0 & male==0, stat(mean sd) col(stat) save

{txt}{ralign 12:variable} {...}
{c |}      mean        sd
{hline 13}{c +}{hline 20}
{ralign 12:total_voters} {...}
{c |}{...}
 {res} 1293.708  456.7233
{txt}{ralign 12:female_vo~rs} {...}
{c |}{...}
 {res} 565.2959  207.2966
{txt}{ralign 12:female_votes} {...}
{c |}{...}
 {res} 284.8306  120.1636
{txt}{ralign 12:male_voters} {...}
{c |}{...}
 {res}   725.62  259.6037
{txt}{ralign 12:male_votes} {...}
{c |}{...}
 {res} 410.8807  147.1738
{txt}{ralign 12:valid_share} {...}
{c |}{...}
 {res} 96.56557  3.045034
{txt}{hline 13}{c BT}{hline 20}

{com}. matrix desc_stat = r(StatTotal)
{txt}
{com}. gen mean_31 = desc_stat[1,1]
{txt}
{com}. gen mean_32 = desc_stat[1,2]
{txt}
{com}. gen mean_33 = desc_stat[1,3]
{txt}
{com}. gen mean_34 = desc_stat[1,4]
{txt}
{com}. gen mean_35 = desc_stat[1,5]
{txt}
{com}. gen mean_36 = desc_stat[1,6]
{txt}
{com}. 
. putexcel E2 = (mean_31) E4 = (mean_32) E6 = (mean_33) E8 = (mean_34) E10 = (mean_35) E12 = (mean_36)  
{res}{txt}file Table_1_new.xlsx saved

{com}. 
. gen sd_31 = desc_stat[2,1]
{txt}
{com}. gen sd_32 = desc_stat[2,2]
{txt}
{com}. gen sd_33 = desc_stat[2,3]
{txt}
{com}. gen sd_34 = desc_stat[2,4]
{txt}
{com}. gen sd_35 = desc_stat[2,5]
{txt}
{com}. gen sd_36 = desc_stat[2,6]
{txt}
{com}. 
. putexcel E3 = (sd_31) E5 = (sd_32) E7 = (sd_33) E9 = (sd_34) E11 = (sd_35) E13 = (sd_36) 
{res}{txt}file Table_1_new.xlsx saved

{com}. 
. restore
{txt}
{com}. 
. * Online Appendix Table 2. Descriptive Statistics by PS Type for PL with All PS Types *
. 
. preserve
{txt}
{com}. 
. putexcel set "Table_2_new.xlsx", sheet(Sheet1) replace
{res}{txt}
{com}. putexcel A1 = ("") B1 = ("Overall")  C1 = ("Female-Only") D1 = ("Male-Only") E1 = ("Combined")
{res}{txt}file Table_2_new.xlsx saved

{com}. putexcel A2 = ("Total Voters") A4 = ("Female Registered Voters") A6 = ("Female Votes Cast") A8 = ("Male Registered Voters") A10 = ("Male Votes Cast") A12 = ("Share of Valid Votes (\%)")  
{res}{txt}file Table_2_new.xlsx saved

{com}. 
. * Overall
. tabstat total_voters female_voters female_votes male_voters male_votes valid_share if assembly_cat==1 & all_types==1, stat(mean sd) col(stat) save

{txt}{ralign 12:variable} {...}
{c |}      mean        sd
{hline 13}{c +}{hline 20}
{ralign 12:total_voters} {...}
{c |}{...}
 {res} 1255.026  364.1587
{txt}{ralign 12:female_vo~rs} {...}
{c |}{...}
 {res} 565.1819  484.5391
{txt}{ralign 12:female_votes} {...}
{c |}{...}
 {res} 248.6651  226.1283
{txt}{ralign 12:male_voters} {...}
{c |}{...}
 {res} 689.8438  574.3745
{txt}{ralign 12:male_votes} {...}
{c |}{...}
 {res} 365.6736  317.6806
{txt}{ralign 12:valid_share} {...}
{c |}{...}
 {res} 97.36583  2.145803
{txt}{hline 13}{c BT}{hline 20}

{com}. matrix desc_stat = r(StatTotal)
{txt}
{com}. gen mean_1 = desc_stat[1,1]
{txt}
{com}. gen mean_2 = desc_stat[1,2]
{txt}
{com}. gen mean_3 = desc_stat[1,3]
{txt}
{com}. gen mean_4 = desc_stat[1,4]
{txt}
{com}. gen mean_5 = desc_stat[1,5]
{txt}
{com}. gen mean_6 = desc_stat[1,6]
{txt}
{com}. 
. putexcel B2 = (mean_1) B4 = (mean_2) B6 = (mean_3) B8 = (mean_4) B10 = (mean_5) B12 = (mean_6)  
{res}{txt}file Table_2_new.xlsx saved

{com}. 
. gen sd_1 = desc_stat[2,1]
{txt}
{com}. gen sd_2 = desc_stat[2,2]
{txt}
{com}. gen sd_3 = desc_stat[2,3]
{txt}
{com}. gen sd_4 = desc_stat[2,4]
{txt}
{com}. gen sd_5 = desc_stat[2,5]
{txt}
{com}. gen sd_6 = desc_stat[2,6]
{txt}
{com}. 
. putexcel B3 = (sd_1) B5 = (sd_2) B7 = (sd_3) B9 = (sd_4) B11 = (sd_5) B13 = (sd_6)  
{res}{txt}file Table_2_new.xlsx saved

{com}. 
. * Female-Only
. tabstat total_voters female_voters female_votes male_voters male_votes valid_share if assembly_cat==1 & female==1 & all_types==1, stat(mean sd) col(stat) save

{txt}{ralign 12:variable} {...}
{c |}      mean        sd
{hline 13}{c +}{hline 20}
{ralign 12:total_voters} {...}
{c |}{...}
 {res} 1086.493  299.0554
{txt}{ralign 12:female_vo~rs} {...}
{c |}{...}
 {res} 1086.493  299.0554
{txt}{ralign 12:female_votes} {...}
{c |}{...}
 {res} 482.2429    164.52
{txt}{ralign 12:male_voters} {...}
{c |}{...}
 {res}        0         0
{txt}{ralign 12:male_votes} {...}
{c |}{...}
 {res}        0         0
{txt}{ralign 12:valid_share} {...}
{c |}{...}
 {res} 96.58101  2.668188
{txt}{hline 13}{c BT}{hline 20}

{com}. matrix desc_stat = r(StatTotal)
{txt}
{com}. gen mean_11 = desc_stat[1,1]
{txt}
{com}. gen mean_12 = desc_stat[1,2]
{txt}
{com}. gen mean_13 = desc_stat[1,3]
{txt}
{com}. gen mean_14 = desc_stat[1,4]
{txt}
{com}. gen mean_15 = desc_stat[1,5]
{txt}
{com}. gen mean_16 = desc_stat[1,6]
{txt}
{com}. 
. putexcel C2 = (mean_11) C4 = (mean_12) C6 = (mean_13) C8 = (mean_14) C10 = (mean_15) C12 = (mean_16)  
{res}{txt}file Table_2_new.xlsx saved

{com}. 
. gen sd_11 = desc_stat[2,1]
{txt}
{com}. gen sd_12 = desc_stat[2,2]
{txt}
{com}. gen sd_13 = desc_stat[2,3]
{txt}
{com}. gen sd_14 = desc_stat[2,4]
{txt}
{com}. gen sd_15 = desc_stat[2,5]
{txt}
{com}. gen sd_16 = desc_stat[2,6]
{txt}
{com}. 
. putexcel C3 = (sd_11) C5 = (sd_12) C7 = (sd_13) C9 = (sd_14) C11 = (sd_15) C13 = (sd_16) 
{res}{txt}file Table_2_new.xlsx saved

{com}. 
. * Male-Only
. tabstat total_voters female_voters female_votes male_voters male_votes valid_share if assembly_cat==1 & male==1 & all_types==1, stat(mean sd) col(stat) save

{txt}{ralign 12:variable} {...}
{c |}      mean        sd
{hline 13}{c +}{hline 20}
{ralign 12:total_voters} {...}
{c |}{...}
 {res} 1299.821  325.9927
{txt}{ralign 12:female_vo~rs} {...}
{c |}{...}
 {res}        0         0
{txt}{ralign 12:female_votes} {...}
{c |}{...}
 {res}        0         0
{txt}{ralign 12:male_voters} {...}
{c |}{...}
 {res} 1299.821  325.9927
{txt}{ralign 12:male_votes} {...}
{c |}{...}
 {res} 698.5097  200.5713
{txt}{ralign 12:valid_share} {...}
{c |}{...}
 {res} 97.85905  1.590606
{txt}{hline 13}{c BT}{hline 20}

{com}. matrix desc_stat = r(StatTotal)
{txt}
{com}. gen mean_21 = desc_stat[1,1]
{txt}
{com}. gen mean_22 = desc_stat[1,2]
{txt}
{com}. gen mean_23 = desc_stat[1,3]
{txt}
{com}. gen mean_24 = desc_stat[1,4]
{txt}
{com}. gen mean_25 = desc_stat[1,5]
{txt}
{com}. gen mean_26 = desc_stat[1,6]
{txt}
{com}. 
. putexcel D2 = (mean_21) D4 = (mean_22) D6 = (mean_23) D8 = (mean_24) D10 = (mean_25) D12 = (mean_26)  
{res}{txt}file Table_2_new.xlsx saved

{com}. 
. gen sd_21 = desc_stat[2,1]
{txt}
{com}. gen sd_22 = desc_stat[2,2]
{txt}
{com}. gen sd_23 = desc_stat[2,3]
{txt}
{com}. gen sd_24 = desc_stat[2,4]
{txt}
{com}. gen sd_25 = desc_stat[2,5]
{txt}
{com}. gen sd_26 = desc_stat[2,6]
{txt}
{com}. 
. putexcel D3 = (sd_21) D5 = (sd_22) D7 = (sd_23) D9 = (sd_24) D11 = (sd_25) D13 = (sd_26) 
{res}{txt}file Table_2_new.xlsx saved

{com}. 
. * Combined
. tabstat total_voters female_voters female_votes male_voters male_votes valid_share if assembly_cat==1 & female==0 & male==0 & all_types==1, stat(mean sd) col(stat) save

{txt}{ralign 12:variable} {...}
{c |}      mean        sd
{hline 13}{c +}{hline 20}
{ralign 12:total_voters} {...}
{c |}{...}
 {res} 1368.232  395.4143
{txt}{ralign 12:female_vo~rs} {...}
{c |}{...}
 {res} 611.7854  193.7343
{txt}{ralign 12:female_votes} {...}
{c |}{...}
 {res} 273.7774  102.5827
{txt}{ralign 12:male_voters} {...}
{c |}{...}
 {res} 756.4467  230.2243
{txt}{ralign 12:male_votes} {...}
{c |}{...}
 {res} 405.7199  134.3443
{txt}{ralign 12:valid_share} {...}
{c |}{...}
 {res} 97.62906  1.836545
{txt}{hline 13}{c BT}{hline 20}

{com}. matrix desc_stat = r(StatTotal)
{txt}
{com}. gen mean_31 = desc_stat[1,1]
{txt}
{com}. gen mean_32 = desc_stat[1,2]
{txt}
{com}. gen mean_33 = desc_stat[1,3]
{txt}
{com}. gen mean_34 = desc_stat[1,4]
{txt}
{com}. gen mean_35 = desc_stat[1,5]
{txt}
{com}. gen mean_36 = desc_stat[1,6]
{txt}
{com}. 
. putexcel E2 = (mean_31) E4 = (mean_32) E6 = (mean_33) E8 = (mean_34) E10 = (mean_35) E12 = (mean_36)  
{res}{txt}file Table_2_new.xlsx saved

{com}. 
. gen sd_31 = desc_stat[2,1]
{txt}
{com}. gen sd_32 = desc_stat[2,2]
{txt}
{com}. gen sd_33 = desc_stat[2,3]
{txt}
{com}. gen sd_34 = desc_stat[2,4]
{txt}
{com}. gen sd_35 = desc_stat[2,5]
{txt}
{com}. gen sd_36 = desc_stat[2,6]
{txt}
{com}. 
. putexcel E3 = (sd_31) E5 = (sd_32) E7 = (sd_33) E9 = (sd_34) E11 = (sd_35) E13 = (sd_36) 
{res}{txt}file Table_2_new.xlsx saved

{com}. 
. restore
{txt}
{com}. 
. * Online Appendix Table 3. Share of Different Types of PS by Province *
. 
. bysort province: summ share_m share_f share_c

{txt}{hline}
-> province = balochistan

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}share_m {c |}{res}      4,310    .2948956    .1225303   .1433962   .5544041
{txt}{space 5}share_f {c |}{res}      4,310    .2526682    .0871618   .1283019   .4393939
{txt}{space 5}share_c {c |}{res}      4,310    .4524362    .2080052   .0090909   .7283019

{txt}{hline}
-> province = fata

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}share_m {c |}{res}      1,171    .2444539    .1465955   .0857143    .517588
{txt}{space 5}share_f {c |}{res}      1,171    .1856795    .1144189   .0857143   .3969849
{txt}{space 5}share_c {c |}{res}      1,171    .5698667    .2533125   .0854271   .8285714

{txt}{hline}
-> province = federal

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}share_m {c |}{res}        570    .4578947    .0042004   .4530612   .4615385
{txt}{space 5}share_f {c |}{res}        570    .4561403    .0097697   .4448979   .4646154
{txt}{space 5}share_c {c |}{res}        570    .0859649    .0139701   .0738462   .1020408

{txt}{hline}
-> province = kp

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}share_m {c |}{res}     12,518    .3291367    .1444252          0   .5700935
{txt}{space 5}share_f {c |}{res}     12,518    .2877466    .1110131          0   .4562212
{txt}{space 5}share_c {c |}{res}     12,518    .3831167    .2541392    .003861          1

{txt}{hline}
-> province = punjab

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}share_m {c |}{res}     43,181    .2658429    .1063188   .0158103   .5212464
{txt}{space 5}share_f {c |}{res}     43,181    .2519987     .098439   .0158103   .4787535
{txt}{space 5}share_c {c |}{res}     43,181    .4821584    .2041508          0   .9683794

{txt}{hline}
-> province = sindh

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}share_m {c |}{res}     17,341    .2167655    .1255108          0   .5335366
{txt}{space 5}share_f {c |}{res}     17,341    .2027687    .1139951          0   .4634146
{txt}{space 5}share_c {c |}{res}     17,341    .5804659    .2379701   .0030488          1

{txt}
{com}. 
. * Online Appendix Table 4. Share of Polling Locations by Type and Province *
. 
. bysort province assembly_cat all_types: egen num_pl = nvals(id)
{txt}(3403 missing values generated)

{com}. by province: summ num_pl if assembly_cat==1 & all_types==1

{txt}{hline}
-> province = balochistan

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}num_pl {c |}{res}          0

{txt}{hline}
-> province = fata

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}num_pl {c |}{res}          3           1           0          1          1

{txt}{hline}
-> province = federal

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}num_pl {c |}{res}         59          18           0         18         18

{txt}{hline}
-> province = kp

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}num_pl {c |}{res}         68          22           0         22         22

{txt}{hline}
-> province = punjab

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}num_pl {c |}{res}      1,133         322           0        322        322

{txt}{hline}
-> province = sindh

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}num_pl {c |}{res}        529         140           0        140        140

{txt}
{com}. 
. bysort province assembly_cat two_types: egen num_pl2 = nvals(id)
{txt}(3403 missing values generated)

{com}. by province: summ num_pl2 if assembly_cat==1 & two_types==1

{txt}{hline}
-> province = balochistan

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}num_pl2 {c |}{res}        181          83           0         83         83

{txt}{hline}
-> province = fata

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}num_pl2 {c |}{res}         47          20           0         20         20

{txt}{hline}
-> province = federal

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}num_pl2 {c |}{res}        506         190           0        190        190

{txt}{hline}
-> province = kp

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}num_pl2 {c |}{res}        895         404           0        404        404

{txt}{hline}
-> province = punjab

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}num_pl2 {c |}{res}      8,754        3746           0       3746       3746

{txt}{hline}
-> province = sindh

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}num_pl2 {c |}{res}      3,103        1357           0       1357       1357

{txt}
{com}. 
. bysort province assembly_cat: egen num_pl3 = nvals(id)
{txt}(3403 missing values generated)

{com}. by province: summ num_pl3 if assembly_cat==1

{txt}{hline}
-> province = balochistan

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}num_pl3 {c |}{res}      4,310        3962           0       3962       3962

{txt}{hline}
-> province = fata

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}num_pl3 {c |}{res}      1,171        1092           0       1092       1092

{txt}{hline}
-> province = federal

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}num_pl3 {c |}{res}        570         252           0        252        252

{txt}{hline}
-> province = kp

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}num_pl3 {c |}{res}     12,518       10902           0      10902      10902

{txt}{hline}
-> province = punjab

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}num_pl3 {c |}{res}     39,778       31638           0      31638      31638

{txt}{hline}
-> province = sindh

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}num_pl3 {c |}{res}     17,341       13702           0      13702      13702

{txt}
{com}. 
. * Online Appendix Table 5. Distribution of Polling Locations with Different Polling Stations *
. 
. preserve
{txt}
{com}. egen n12 = nvals(id) if n_comb==0 & n_male+n_female==1
{txt}(58476 missing values generated)

{com}. egen n13 = nvals(id) if n_comb==0 & n_male+n_female==2
{txt}(66466 missing values generated)

{com}. egen n14 = nvals(id) if n_comb==0 & n_male+n_female>=3
{txt}(75791 missing values generated)

{com}. 
. egen n21 = nvals(id) if n_comb==1 & n_male==0 & n_female==0
{txt}(49806 missing values generated)

{com}. egen n22 = nvals(id) if n_comb==1 & n_male+n_female==1
{txt}(78201 missing values generated)

{com}. egen n23 = nvals(id) if n_comb==1 & n_male+n_female==2
{txt}(77924 missing values generated)

{com}. egen n24 = nvals(id) if n_comb==1 & n_male+n_female>=3
{txt}(78761 missing values generated)

{com}. 
. egen n31 = nvals(id) if n_comb==2 & n_male==0 & n_female==0
{txt}(74245 missing values generated)

{com}. egen n32 = nvals(id) if n_comb==2 & n_male+n_female==1
{txt}(78980 missing values generated)

{com}. egen n33 = nvals(id) if n_comb==2 & n_male+n_female==2
{txt}(78879 missing values generated)

{com}. egen n34 = nvals(id) if n_comb==2 & n_male+n_female>=3
{txt}(78995 missing values generated)

{com}. 
. egen n41 = nvals(id) if n_comb>=3 & n_male==0 & n_female==0
{txt}(77624 missing values generated)

{com}. egen n42 = nvals(id) if n_comb>3 & n_male+n_female==1
{txt}(79091 missing values generated)

{com}. egen n43 = nvals(id) if n_comb>=3 & n_male+n_female==2
{txt}(78987 missing values generated)

{com}. egen n44 = nvals(id) if n_comb>=3 & n_male+n_female>=3
{txt}(78949 missing values generated)

{com}. 
. summ n12-n14 n21-n24 n31-n34 n41-n44

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}n12 {c |}{res}     20,615       20611           0      20611      20611
{txt}{space 9}n13 {c |}{res}     12,625        6312           0       6312       6312
{txt}{space 9}n14 {c |}{res}      3,300         935           0        935        935
{txt}{space 9}n21 {c |}{res}     29,285       29281           0      29281      29281
{txt}{space 9}n22 {c |}{res}        890         445           0        445        445
{txt}{hline 13}{c +}{hline 57}
{space 9}n23 {c |}{res}      1,167         389           0        389        389
{txt}{space 9}n24 {c |}{res}        330          68           0         68         68
{txt}{space 9}n31 {c |}{res}      4,846        2423           0       2423       2423
{txt}{space 9}n32 {c |}{res}        111          37           0         37         37
{txt}{space 9}n33 {c |}{res}        212          53           0         53         53
{txt}{hline 13}{c +}{hline 57}
{space 9}n34 {c |}{res}         96          17           0         17         17
{txt}{space 9}n41 {c |}{res}      1,467         412           0        412        412
{txt}{space 9}n42 {c |}{res}          0
{txt}{space 9}n43 {c |}{res}        104          18           0         18         18
{txt}{space 9}n44 {c |}{res}        142          80           0         80         80
{txt}
{com}. restore 
{txt}
{com}. 
. * Table 6. Lower Turnout for Males in Separate-Gender Polling Stations*
. 
. * Full Sample Regressions: Columns 1 and 2 *
. eststo clear
{txt}
{com}. 
. reg male_turnout_ps male if assembly_cat==1, cluster(id)

{txt}Linear regression                               Number of obs     = {res}    53,354
                                                {txt}F(1, 46823)       =  {res}   184.39
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0038
                                                {txt}Root MSE          =    {res} .10597

{txt}{ralign 78:(Std. Err. adjusted for {res:46,824} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}male_turno~s{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}male {c |}{col 14}{res}{space 2}-.0136193{col 26}{space 2}  .001003{col 37}{space 1}  -13.58{col 46}{space 3}0.000{col 54}{space 4}-.0155852{col 67}{space 3}-.0116535
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5714115{col 26}{space 2} .0006857{col 37}{space 1}  833.35{col 46}{space 3}0.000{col 54}{space 4} .5700675{col 67}{space 3} .5727554
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. su male_turnout_ps if e(sample) & assembly_cat==1 & male==0

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
male_turno~s {c |}{res}     34,387    .5714115    .1122354          0          1
{txt}
{com}. estadd local building "No"

{txt}added macro:
           e(building) : "{res:No}"

{com}. eststo Model1
{txt}
{com}. estimates store main_1_m
{txt}
{com}. 
. areg male_turnout_ps male if assembly_cat==1, cluster(id) absorb(id)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    53,354
{txt}{col 49}F({res}   1{txt},{res}  46823{txt}){col 67}= {res}      1.94
{txt}{col 49}Prob > F{col 67}= {res}    0.1638
{txt}{col 49}R-squared{col 67}= {res}    0.9686
{txt}{col 49}Adj R-squared{col 67}= {res}    0.7430
{txt}{col 49}Root MSE{col 67}= {res}    0.0538

{txt}{ralign 78:(Std. Err. adjusted for {res:46,824} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}male_turno~s{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}male {c |}{col 14}{res}{space 2}-.0099726{col 26}{space 2} .0071626{col 37}{space 1}   -1.39{col 46}{space 3}0.164{col 54}{space 4}-.0240114{col 67}{space 3} .0040663
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5701151{col 26}{space 2} .0025463{col 37}{space 1}  223.90{col 46}{space 3}0.000{col 54}{space 4} .5651243{col 67}{space 3} .5751058
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          id {c |}   absorbed                                   (46824 categories)

{com}. su male_turnout_ps if e(sample) & assembly_cat==1 & male==0

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
male_turno~s {c |}{res}     34,387    .5714115    .1122354          0          1
{txt}
{com}. estadd local building "Yes"

{txt}added macro:
           e(building) : "{res:Yes}"

{com}. eststo Model2
{txt}
{com}. estimates store main_2_m
{txt}
{com}. 
. * Restricted Sample Regressions (by location type): Columns 3 & 4 *
. 
. areg male_turnout_ps male if assembly_cat==1 & two_types==1, cluster(id) absorb(id)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     7,303
{txt}{col 49}F({res}   1{txt},{res}   5659{txt}){col 67}= {res}      3.56
{txt}{col 49}Prob > F{col 67}= {res}    0.0592
{txt}{col 49}R-squared{col 67}= {res}    0.9311
{txt}{col 49}Adj R-squared{col 67}= {res}    0.6937
{txt}{col 49}Root MSE{col 67}= {res}    0.0516

{txt}{ralign 78:(Std. Err. adjusted for {res:5,660} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}male_turno~s{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}male {c |}{col 14}{res}{space 2}-.0099726{col 26}{space 2} .0052843{col 37}{space 1}   -1.89{col 46}{space 3}0.059{col 54}{space 4}-.0203317{col 67}{space 3} .0003866
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5719804{col 26}{space 2} .0044652{col 37}{space 1}  128.10{col 46}{space 3}0.000{col 54}{space 4} .5632269{col 67}{space 3} .5807338
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          id {c |}   absorbed                                    (5660 categories)

{com}. su male_turnout_ps if e(sample) & assembly_cat==1 & male==0 & two_types==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
male_turno~s {c |}{res}      1,132    .5507733    .1066908          0          1
{txt}
{com}. estadd local building "Yes"

{txt}added macro:
           e(building) : "{res:Yes}"

{com}. eststo Model3
{txt}
{com}. estimates store main_3_m
{txt}
{com}. 
. areg male_turnout_ps male if assembly_cat==1 & all_types==1, cluster(id) absorb(id)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     1,147
{txt}{col 49}F({res}   1{txt},{res}    502{txt}){col 67}= {res}      3.01
{txt}{col 49}Prob > F{col 67}= {res}    0.0834
{txt}{col 49}R-squared{col 67}= {res}    0.8292
{txt}{col 49}Adj R-squared{col 67}= {res}    0.6956
{txt}{col 49}Root MSE{col 67}= {res}    0.0562

{txt}{ralign 78:(Std. Err. adjusted for {res:503} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}male_turno~s{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}male {c |}{col 14}{res}{space 2}-.0081148{col 26}{space 2} .0046783{col 37}{space 1}   -1.73{col 46}{space 3}0.083{col 54}{space 4}-.0173062{col 67}{space 3} .0010767
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5464196{col 26}{space 2} .0023045{col 37}{space 1}  237.11{col 46}{space 3}0.000{col 54}{space 4}  .541892{col 67}{space 3} .5509472
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          id {c |}   absorbed                                     (503 categories)

{com}. su male_turnout_ps if e(sample) & assembly_cat==1 & male==0 & all_types==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
male_turno~s {c |}{res}        582    .5431623    .1087531     .02781          1
{txt}
{com}. estadd local building "Yes"

{txt}added macro:
           e(building) : "{res:Yes}"

{com}. eststo Model4
{txt}
{com}. estimates store main_4_m
{txt}
{com}. 
. esttab Model1 Model2 Model3 Model4 using turnout_male.tex, label replace booktabs keep(male) ///
>  b(3) se(3) sfmt(3)  title(Male Turnout\label{c -(}tab:turnout_male{c )-}) ///
>  scalars("r2_a Adjusted R\textsuperscript{c -(}2{c )-}" "building Location Fixed Effects") mtitle("" "" "" "") ///
>  unstack collabels(none) order(male) star(* 0.10 ** 0.05 *** 0.01)
{res}{txt}(note: file turnout_male.tex not found)
(output written to {browse  `"turnout_male.tex"'})

{com}. 
. * PART 3: FIGURES * 
. 
. * Figure 1. Gender Gap in Turnout in Pakistan *
. 
. *C. DISTRIBUTIONS
. 
. twoway kdensity female_turnout_ps, bw(0.1) xtitle("Turnout") ytitle("Density") xtick(#13) xlabel(0 (0.2) 1, format(%8.1f)) legend(label(1 "Female"))  legend(label(2 "Male")) lpattern(solid)  lwidth(medthick medthick) || kdensity male_turnout_ps, bw(0.1) lpattern(shortdash) lcolor(orange*1.5)
{res}{txt}
{com}. 
. graph export "dist_turnout.png", as(png) replace
{txt}(file dist_turnout.png written in PNG format)

{com}. 
. * Figure 2. Combined Polling Stations Have Higher Turnout than Separate-Gender Ones *
. 
. *A. TURNOUT DISTRIBUTION FOR FEMALES AND MALES BY PS TYPE
. 
. twoway kdensity female_turnout_ps if ps_gender_type=="Combined", bw(0.1) xtitle("Female Turnout") ytitle("Density") xtick(#13) xlabel(0 (0.2) 1, format(%8.1f)) legend(label(1 "Combined PS"))  legend(label(2 "Female Only PS")) lpattern(solid)  lwidth(medthick medthick) || kdensity female_turnout_ps if ps_gender_type=="Female only", bw(0.1) lpattern(shortdash) lcolor(orange*1.5) name(female_turnout_pstype, replace)
{res}{txt}
{com}. 
. twoway kdensity male_turnout_ps if ps_gender_type=="Combined", bw(0.1) xtitle("Male Turnout") ytitle("Density") xtick(#13) xlabel(0 (0.2) 1, format(%8.1f)) legend(label(1 "Combined PS"))  legend(label(2 "Male Only PS")) lpattern(solid)  lwidth(medthick medthick) || kdensity male_turnout_ps if ps_gender_type=="Male only", bw(0.1) lpattern(shortdash) lcolor(orange*1.5) name(male_turnout_pstype, replace) yscale(off) fxsize(90)
{res}{txt}
{com}. 
. graph combine female_turnout_pstype male_turnout_pstype, cols(2) iscale(1)  xsize(8) xcommon ycommon
{res}{txt}
{com}. 
. graph export "turnout_pstype.png", as(png) replace
{txt}(file turnout_pstype.png written in PNG format)

{com}. 
. * Online Appendix Figure 1. Scatter Plots of Constituency Characteristics against Share of Combined PS in a Constituency *
. 
. scatter constituency_registered_voters_t n_comb_share_cons if assembly_cat==1, msize(tiny) ///
> xtitle("% Share of Combined PS in Constituency") ytitle("Total Number of Registered Voters in Constituency") ///
> ylabel(, labsize(small)) xlabel(, labsize(small))
{res}{txt}
{com}. graph export "totalregvoters_cons.png", as(png) replace
{txt}(file totalregvoters_cons.png written in PNG format)

{com}. 
. scatter n_candidates n_comb_share_cons if assembly_cat==1, msize(tiny) ///
> xtitle("% Share of Combined PS in Constituency") ytitle("Total Number of Candidates in Constituency") ///
> ylabel(, labsize(small)) xlabel(, labsize(small))
{res}{txt}
{com}. graph export "totalcandidates_cons.png", as(png) replace
{txt}(file totalcandidates_cons.png written in PNG format)

{com}. 
. scatter constituency_number_ps_total n_comb_share_cons if assembly_cat==1, msize(tiny) ///
> xtitle("% Share of Combined PS in Constituency") ytitle("Total Number of PS in Constituency") ///
> ylabel(, labsize(small)) xlabel(, labsize(small))
{res}{txt}
{com}. graph export "totalps_cons.png", as(png) replace
{txt}(file totalps_cons.png written in PNG format)

{com}. 
. * Online Appendix Figure 2. Scatter Plots of Constituency Characteristics against Share of Female-Only PS in a Constituency *
. 
. scatter constituency_registered_voters_f n_f_share_cons if assembly_cat==1, msize(tiny) ///
> xtitle("% Share of Female Only PS in Constituency") ytitle("Total Number of Registered Female Voters in Constituency") ///
> ylabel(, labsize(small)) xlabel(, labsize(small))
{res}{txt}
{com}. graph export "totalregvoters_consf.png", as(png) replace
{txt}(file totalregvoters_consf.png written in PNG format)

{com}. 
. scatter n_candidates n_f_share_cons if assembly_cat==1, msize(tiny) ///
> xtitle("% Share of Female Only PS in Constituency") ytitle("Total Number of Candidates in Constituency") ///
> ylabel(, labsize(small)) xlabel(, labsize(small)) 
{res}{txt}
{com}. graph export "totalcandidates_consf.png", as(png) replace
{txt}(file totalcandidates_consf.png written in PNG format)

{com}. 
. scatter constituency_number_ps_total n_f_share_cons if assembly_cat==1, msize(tiny) ///
> xtitle("% Share of Female Only PS in Constituency") ytitle("Total Number of PS in Constituency") ///
> ylabel(, labsize(small)) xlabel(, labsize(small))
{res}{txt}
{com}. graph export "totalps_consf.png", as(png) replace
{txt}(file totalps_consf.png written in PNG format)

{com}. 
. log close 
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/shrutilakhtakia/Desktop/Replication Files/replication.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}26 Aug 2023, 08:48:00
{txt}{.-}
{smcl}
{txt}{sf}{ul off}